Abstract
Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer travelers better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this problem, a bi-level mathematical programming formulation and solution algorithm are presented for the joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP). The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable. The lower-level problem consists of a dynamic combined mode choiceâ??traveler assignment problem (DCMC-TAP) formulation. The solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based simulation-assignment approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the regionâ??s existing multimodal transit network. The results indicate the ability of the solution procedure to solve the bi-level JTNR-SFSDP. Moreover, computational results indicate significant traveler benefits associated with optimizing the joint design of multimodal transit networks and SAMS fleets.